| Artifact | Value | |
|---|---|---|
| 0 | Phase 1 OOF rows | 154386 |
| 1 | Phase 1-lite OOF rows | 154386 |
| 2 | Phase 2 OOF rows | 154386 |
| 3 | Elastic Net OOF rows | 154386 |
| 4 | Random Forest OOF rows | 154386 |
| 5 | ETS OOF rows | 154386 |
| 6 | AdaBoost OOF rows | 154386 |
Walmart Weekly Sales Forecasting: Model Comparison
1 Objective
This report compares six forecasting approaches for weekly Walmart sales:
- Local Linear Anomaly Model (Phase 1).
- Bayesian Structural AR (Phase 2, full data).
- Elastic Net anomaly baseline (Phase 3).
- Random Forest anomaly baseline (Phase 4).
- ETS anomaly baseline (Phase 5).
- AdaBoost anomaly baseline (Phase 7).
Additionally, we include a Phase 1-lite diagnostic variant in the final comparison to show the impact of removing the extra exogenous variables from Local Linear.
Evaluation uses forward-chaining validation and weighted MAE (WMAE).
2 Data and Evaluation Setup
Validation metric:
\[ \text{WMAE} = \frac{\sum_i w_i \lvert y_i - \hat{y}_i \rvert}{\sum_i w_i}, \quad w_i = \begin{cases} 5, & \text{if holiday} \\ 1, & \text{otherwise} \end{cases} \]
3 Model 1: Local Linear Anomaly (Phase 1)
Local Linear is a weighted regression that learns a different local coefficient vector for each week-of-year neighborhood and each series. It is simple and interpretable, but sensitive to feature scaling, so standardization is applied before fitting.
3.1 Mathematical Formulation
For series (s) and target seasonal week (w):
\[ \hat{\beta}_{s,w} = \arg\min_{\beta} \sum_{i \in s} K_h\!\left(d(\text{week}_i,w)\right) \left(y_i - \beta_0 - x_i^\top \beta\right)^2 + \lambda \|\beta\|_2^2 \]
and recursive prediction:
\[ \hat{y}^{anom}_{t,s}=\beta_0 + x_{t,s}^\top \beta,\qquad \hat{y}_{t,s}=\hat{y}^{anom}_{t,s}+clim_{t,s} \]
3.2 Plain-Language Meaning of Terms
series s: one specificStore + Depttime series.week_iandw: historical week index and target seasonal week where local fitting is centered.K_h(d(.)): a weight that gives more importance to rows close in seasonal calendar (e.g., nearby weeks of year).x_i: the input features for a row (lags, holiday flag, exogenous variables).beta_0, beta: intercept and coefficients learned by local weighted regression.lambda: regularization strength that shrinks coefficients to avoid unstable fits.y^{anom}: anomaly target (real sales minus climatology baseline).clim_{t,s}: baseline seasonal level added back to convert anomaly prediction to sales prediction.
3.3 Workflow
flowchart LR A[Read train/test parquet] --> B[Compute climatology per series/store/week] B --> C[Create anomalies and lag1/lag2] C --> D[Impute missing lag/exogenous values] D --> E[Standard-scale Phase 1 features] E --> F[Forward-chaining CV] F --> G[Fit local weighted ridge by series and seasonal week] G --> H[Recursive rollout on validation horizon] H --> I[OOF metrics and predictions] I --> J[Fit full train and forecast test]
3.4 Tuned Parameters and Effect
| Parameter | Value | How it works | |
|---|---|---|---|
| 0 | kernel | tricube | Kernel shape over seasonal distance; controls ... |
| 1 | bandwidth | 6 | Neighborhood width in week-of-year units; larg... |
| 2 | min_samples | 16 | Minimum active samples to fit a local model; g... |
| 3 | ridge | 0.0001 | L2 regularization strength in weighted regress... |
| 4 | coef_clip | 6.0 | Post-fit coefficient clipping to prevent extre... |
| 5 | anom_clip_scale | 2.0 | Prediction clipping band around anomaly quanti... |
| 6 | lags | [1, 2] | Autoregressive inputs used in recursive foreca... |
| 7 | standard_scaler | True | Feature standardization before fitting local r... |
{'kernel': 'tricube',
'bandwidth': 6,
'min_samples': 16,
'ridge': 0.0001,
'coef_clip': 6.0,
'anom_clip_scale': 2.0,
'lags': [1, 2],
'use_interactions': False,
'interaction_cols': [],
'feature_cols': ['temp_anom',
'fuel_anom',
'MarkDown1',
'MarkDown2',
'MarkDown3',
'MarkDown4',
'MarkDown5',
'CPI',
'Unemployment',
'sales_anom_lag1',
'sales_anom_lag2',
'is_holiday_int'],
'standard_scaler': True,
'n_folds': 4,
'val_weeks': 13,
'max_series': None}
| fold | train_start | train_end | val_start | val_end | wmae | mae | rmse | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2010-02-05 | 2011-10-28 | 2011-11-04 | 2012-01-27 | 5989.774614 | 5916.085624 | 11465.394577 |
| 1 | 2 | 2010-02-05 | 2012-01-27 | 2012-02-03 | 2012-04-27 | 10061.944470 | 10472.387681 | 20457.695819 |
| 2 | 3 | 2010-02-05 | 2012-04-27 | 2012-05-04 | 2012-07-27 | 11956.737489 | 11956.737489 | 21420.299578 |
| 3 | 4 | 2010-02-05 | 2012-07-27 | 2012-08-03 | 2012-10-26 | 12294.668865 | 12479.604773 | 22343.880598 |
wmae 10075.78136
mae 10206.203892
rmse 18921.817643
Name: 4, dtype: object
4 Model 1-lite: Local Linear Diagnostic Variant
Phase 1-lite keeps the same Local Linear method but removes the extra exogenous variables (MarkDown1-5, CPI, Unemployment) and uses only temp_anom, fuel_anom plus lags/holiday.
{'kernel': 'tricube',
'bandwidth': 6,
'min_samples': 16,
'ridge': 0.0001,
'coef_clip': 6.0,
'anom_clip_scale': 2.0,
'lags': [1, 2],
'feature_mode': 'lite',
'exogenous_features': ['temp_anom', 'fuel_anom'],
'use_interactions': False,
'interaction_cols': [],
'feature_cols': ['temp_anom',
'fuel_anom',
'sales_anom_lag1',
'sales_anom_lag2',
'is_holiday_int'],
'standard_scaler': True,
'n_folds': 4,
'val_weeks': 13,
'max_series': None}
| fold | train_start | train_end | val_start | val_end | wmae | mae | rmse | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2010-02-05 | 2011-10-28 | 2011-11-04 | 2012-01-27 | 2468.567458 | 2271.802436 | 4955.291247 |
| 1 | 2 | 2010-02-05 | 2012-01-27 | 2012-02-03 | 2012-04-27 | 2199.682469 | 2196.184633 | 4485.720535 |
| 2 | 3 | 2010-02-05 | 2012-04-27 | 2012-05-04 | 2012-07-27 | 2028.753667 | 2028.753667 | 3948.262807 |
| 3 | 4 | 2010-02-05 | 2012-07-27 | 2012-08-03 | 2012-10-26 | 1947.742271 | 1923.235682 | 3954.874629 |
wmae 2161.186467
mae 2104.994105
rmse 4336.037305
Name: 4, dtype: object
5 Model 2: Bayesian Structural AR (Phase 2)
Structural AR combines hierarchical intercepts, autoregressive lags, exogenous drivers, and seasonal Fourier terms in one probabilistic model. It is designed to capture both shared structure and series-specific behavior.
5.1 Mathematical Formulation
In z-score space:
\[ y^{*}_{t,s} \sim \mathcal{N}(\mu_{t,s}, \sigma) \]
\[ \mu_{t,s} = \alpha_s + \beta_{lag}^\top lag_{t,s} + \beta_{exog}^\top exog_{t,s} + \beta_h h_{t,s} + \beta_{tr} trend_t + \beta_f^\top fourier_t \]
with hierarchical intercept:
\[ \alpha_s = \alpha_\mu + \alpha_\sigma z_s,\qquad z_s \sim \mathcal{N}(0,1) \]
5.2 Plain-Language Meaning of Terms
y*_{t,s}: normalized anomaly target for weektand seriess.mu_{t,s}: model mean prediction before adding random noise.alpha_s: series-specific baseline level; eachStore + Depthas its own intercept.beta_lag,lag_{t,s}: coefficients and lag features that capture autoregressive behavior.beta_exog,exog_{t,s}: effects of external drivers (temperature, markdown flags, CPI, unemployment, etc.).beta_h h_{t,s}: holiday contribution.beta_tr trend_t: long-run drift over time.beta_f fourier_t: smooth yearly seasonal pattern via sine/cosine terms.sigma: predictive noise level around the mean.
5.3 Workflow
flowchart LR A[Read train parquet] --> B[Compute climatology] B --> C[Create anomaly target and lag1/lag2] C --> D[Build trend and Fourier seasonal terms] D --> E[Fill exogenous NA from train medians] E --> F[Standardize lag/exogenous/trend variables] F --> G[Fit PyMC structural model via MAP] G --> H[Recursive probabilistic rollout by date] H --> I[Aggregate draws to mean/sd and intervals] I --> J[CV metrics and OOF outputs]
5.4 Tuned Parameters and Effect
| Parameter | Value | How it works | |
|---|---|---|---|
| 0 | max_eval | 1800 | Maximum evaluations in MAP optimization; highe... |
| 1 | pred_draws | 40 | Number of stochastic recursive draws for predi... |
| 2 | fourier_order | 3 | Number of sine/cosine seasonal harmonics. |
| 3 | lag_orders | [1, 2] | Autoregressive lags used as structural predict... |
| 4 | sigma_clusters | 0 | Number of heteroskedastic noise clusters; 0 me... |
| 5 | exogenous_features | [temp_anom, fuel_anom, MarkDown1, MarkDown2, M... | External covariates entering the structural mean. |
{'n_folds': 4,
'val_weeks': 13,
'max_eval': 1800,
'random_seed': 8927,
'pred_draws': 40,
'fourier_order': 3,
'lag_orders': [1, 2],
'exogenous_features': ['temp_anom',
'fuel_anom',
'MarkDown1',
'MarkDown2',
'MarkDown3',
'MarkDown4',
'MarkDown5',
'CPI',
'Unemployment'],
'sigma_clusters': 0,
'max_series': 3331,
'note': 'Structural AR model with hierarchical series intercept, trend, Fourier seasonality, and recursive validation.'}
| fold | train_start | train_end | val_start | val_end | wmae | mae | rmse | runtime_sec | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2010-02-05 | 2011-10-28 | 2011-11-04 | 2012-01-27 | 2514.047613 | 2354.646140 | 4935.317829 | 64.718200 |
| 1 | 2 | 2010-02-05 | 2012-01-27 | 2012-02-03 | 2012-04-27 | 2192.877850 | 2207.494598 | 4400.426532 | 68.368072 |
| 2 | 3 | 2010-02-05 | 2012-04-27 | 2012-05-04 | 2012-07-27 | 2138.504577 | 2138.504577 | 3799.486079 | 68.047948 |
| 3 | 4 | 2010-02-05 | 2012-07-27 | 2012-08-03 | 2012-10-26 | 1904.502883 | 1885.828366 | 3620.883291 | 65.612177 |
wmae 2187.483231
mae 2146.61842
rmse 4189.028433
runtime_sec 66.686599
Name: 4, dtype: object
6 Model 3: Elastic Net Anomaly Baseline (Phase 3)
Elastic Net is a linear model with combined L1/L2 regularization. It is a robust baseline for correlated tabular features and keeps an interpretable global linear structure.
6.1 Mathematical Formulation
\[ \hat{\beta} = \arg\min_{\beta} \frac{1}{2n}\|y - X\beta\|_2^2 + \alpha\left( \frac{1-l1\_ratio}{2}\|\beta\|_2^2 + l1\_ratio\|\beta\|_1 \right) \]
6.2 Plain-Language Meaning of Terms
X beta: linear combination of all features.||y - X beta||^2: fit error term (how far predictions are from truth).alpha: total regularization strength.l1_ratio: balance between:- L1 penalty: pushes less-useful coefficients to exactly zero (feature selection behavior).
- L2 penalty: smoothly shrinks coefficients for stability under correlated features.
6.3 Workflow
flowchart LR A[Read train/test parquet] --> B[Compute climatology and anomalies] B --> C[Create lag1/lag2 and calendar features] C --> D[Build exogenous feature matrix] D --> E[Median imputation + standard scaling] E --> F[Fit Elastic Net on train folds] F --> G[Recursive forecasting on validation horizon] G --> H[OOF metrics and predictions] H --> I[Fit full train and forecast test]
6.4 Tuned Parameters and Effect
| Parameter | Value | How it works | |
|---|---|---|---|
| 0 | alpha | 0.02 | Overall regularization strength; larger values... |
| 1 | l1_ratio | 0.2 | Mix between L1 (sparsity) and L2 (stability). |
| 2 | max_iter | 10000 | Maximum coordinate-descent iterations for conv... |
| 3 | lags | [1, 2] | Lag features used in recursive setup. |
| 4 | features | [lag1, lag2, week_of_year, month, is_holiday_i... | Full tabular interface for model fitting. |
{'model': 'elastic_net',
'target': 'sales_anom',
'features': ['lag1',
'lag2',
'week_of_year',
'month',
'is_holiday_int',
'temp_anom',
'fuel_anom',
'MarkDown1',
'MarkDown2',
'MarkDown3',
'MarkDown4',
'MarkDown5',
'CPI',
'Unemployment'],
'lags': [1, 2],
'alpha': 0.02,
'l1_ratio': 0.2,
'max_iter': 10000,
'n_folds': 4,
'val_weeks': 13,
'max_series': None,
'train_path': 'train_feat.parquet',
'test_path': 'test_feat.parquet'}
| fold | train_start | train_end | val_start | val_end | wmae | mae | rmse | runtime_sec | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2010-02-05 | 2011-10-28 | 2011-11-04 | 2012-01-27 | 2472.678568 | 2292.791482 | 4919.584363 | 6.225060 |
| 1 | 2 | 2010-02-05 | 2012-01-27 | 2012-02-03 | 2012-04-27 | 2163.772249 | 2175.479837 | 4420.761374 | 8.105888 |
| 2 | 3 | 2010-02-05 | 2012-04-27 | 2012-05-04 | 2012-07-27 | 2058.784472 | 2058.784472 | 3793.832714 | 7.717091 |
| 3 | 4 | 2010-02-05 | 2012-07-27 | 2012-08-03 | 2012-10-26 | 1833.856224 | 1803.861985 | 3645.865425 | 12.398858 |
wmae 2132.272878
mae 2082.729444
rmse 4195.010969
runtime_sec 8.611724
Name: 4, dtype: object
7 Model 4: Random Forest Anomaly Baseline (Phase 4)
Random Forest is an ensemble of decision trees trained on bootstrap samples. It captures nonlinear interactions with little feature engineering and is robust to mixed feature scales.
7.1 Mathematical Formulation
For (B) trees:
\[ \hat{y}(x)=\frac{1}{B}\sum_{b=1}^{B} T_b(x) \]
where each (T_b) is grown on a bootstrap sample and split candidates are randomized via max_features.
7.2 Plain-Language Meaning of Terms
T_b(x): prediction from treeb.B: number of trees in the forest.- Final prediction is an average of trees, which reduces variance and improves robustness.
- Bootstrap sampling means each tree sees a slightly different sampled training set.
- Random split-feature selection (
max_features) decorrelates trees, improving ensemble quality.
7.3 Workflow
flowchart LR A[Read train/test parquet] --> B[Compute climatology and anomalies] B --> C[Create lag1/lag2 + calendar + exogenous features] C --> D[Median imputation] D --> E[Train Random Forest on fold train] E --> F[Recursive multi-step rollout on fold validation] F --> G[OOF metrics and predictions] G --> H[Refit on full train and forecast test]
7.4 Tuned Parameters and Effect
| Parameter | Value | How it works | |
|---|---|---|---|
| 0 | n_estimators | 120 | Number of trees; larger reduces variance but i... |
| 1 | max_depth | 18 | Maximum tree depth; controls model complexity. |
| 2 | min_samples_leaf | 2 | Minimum samples per leaf; regularizes tree par... |
| 3 | max_features | sqrt | Feature subset size considered at each split. |
| 4 | lags | [1, 2] | Autoregressive lag features used recursively. |
{'model': 'random_forest',
'target': 'sales_anom',
'features': ['lag1',
'lag2',
'week_of_year',
'month',
'is_holiday_int',
'temp_anom',
'fuel_anom',
'MarkDown1',
'MarkDown2',
'MarkDown3',
'MarkDown4',
'MarkDown5',
'CPI',
'Unemployment'],
'lags': [1, 2],
'n_estimators': 120,
'max_depth': 18,
'min_samples_leaf': 2,
'max_features': 'sqrt',
'n_folds': 4,
'val_weeks': 13,
'max_series': None,
'train_path': 'train_feat.parquet',
'test_path': 'test_feat.parquet'}
| fold | train_start | train_end | val_start | val_end | wmae | mae | rmse | runtime_sec | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2010-02-05 | 2011-10-28 | 2011-11-04 | 2012-01-27 | 2443.770022 | 2243.909149 | 4898.711983 | 14.831643 |
| 1 | 2 | 2010-02-05 | 2012-01-27 | 2012-02-03 | 2012-04-27 | 2132.907277 | 2148.134375 | 4393.405264 | 20.781198 |
| 2 | 3 | 2010-02-05 | 2012-04-27 | 2012-05-04 | 2012-07-27 | 1915.343992 | 1915.343992 | 3675.397679 | 19.938103 |
| 3 | 4 | 2010-02-05 | 2012-07-27 | 2012-08-03 | 2012-10-26 | 1738.982368 | 1719.540629 | 3554.458799 | 19.019421 |
wmae 2057.750915
mae 2006.732036
rmse 4130.493432
runtime_sec 18.642591
Name: 4, dtype: object
8 Model 5: ETS Anomaly Baseline (Phase 5)
ETS here is used on residual anomalies after removing an exogenous linear component. This hybrid keeps classical exponential smoothing dynamics while allowing external regressors to explain systematic variation.
8.1 Mathematical Formulation
Exogenous residual decomposition:
\[ y^{anom}_{t,s}=g(x_{t,s}) + r_{t,s},\qquad g(\cdot)\text{ from Ridge regression} \]
Residual component (r_{t,s}) is modeled by ETS:
\[ r_{t,s} = \ell_{t-1,s}+b_{t-1,s}+s_{t-m,s}+\varepsilon_{t,s} \]
and final forecast:
\[ \hat{y}_{t,s}=clim_{t,s}+\hat{g}(x_{t,s})+\hat{r}_{t,s} \]
8.2 Plain-Language Explanation (What ETS Is)
ETS stands for Error, Trend, Seasonality. It is a classical time-series model that keeps three internal states and updates them week by week:
level: the current baseline sales level for the series.trend: the direction/slope (increasing or decreasing behavior).seasonality: repeating seasonal pattern (here around yearly weekly cycle).
In this project we first predict the anomaly using exogenous variables (g(x)), then ETS models the remaining residual pattern (r). The final prediction is:
- exogenous part
- plus ETS residual dynamics
- plus climatology baseline to return to sales scale.
8.3 Plain-Language Meaning of Terms
y^{anom}: anomaly sales target.g(x): exogenous linear predictor (Ridge) from external variables.r_{t,s}: residual anomaly after removing exogenous part.ell: level state.b: trend state.s: seasonal state.epsilon: random error term.
8.4 Workflow
flowchart LR A[Read train/test parquet] --> B[Compute climatology and anomalies] B --> C[Fit exogenous Ridge on anomaly target] C --> D[Compute residual anomaly = target - exogenous prediction] D --> E[Fit per-series ETS on residuals with fallback candidates] E --> F[Recursive fold forecasting by series] F --> G[Add exogenous component + climatology back] G --> H[OOF metrics and test forecasts]
8.5 Tuned Parameters and Effect
| Parameter | Value | How it works | |
|---|---|---|---|
| 0 | seasonal_periods | 52 | Season length used by ETS seasonal state. |
| 1 | exog_alpha | 1.0 | Ridge penalty for exogenous linear component. |
| 2 | exogenous_features | [temp_anom, fuel_anom, MarkDown1, MarkDown2, M... | Regressors used before ETS residual modeling. |
| 3 | n_folds | 4 | Forward-chaining fold count. |
| 4 | val_weeks | 13 | Validation horizon length per fold. |
{'model': 'ets',
'target': 'sales_anom (via exogenous + ETS residual)',
'seasonal_periods': 52,
'exogenous_features': ['temp_anom',
'fuel_anom',
'MarkDown1',
'MarkDown2',
'MarkDown3',
'MarkDown4',
'MarkDown5',
'CPI',
'Unemployment'],
'exog_alpha': 1.0,
'n_folds': 4,
'val_weeks': 13,
'max_series': None,
'cv_mode_counts': {'level': 10599,
'level-trend': 1796,
'global-fallback': 77,
'last-value-fallback': 51,
'level-seasonal': 4},
'test_mode_counts': {'level': 2758,
'level-trend': 372,
'level-seasonal': 8,
'global-fallback': 11,
'last-value-fallback': 20},
'train_path': 'train_feat.parquet',
'test_path': 'test_feat.parquet'}
| fold | train_start | train_end | val_start | val_end | wmae | mae | rmse | runtime_sec | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2010-02-05 | 2011-10-28 | 2011-11-04 | 2012-01-27 | 2350.860561 | 2193.524621 | 4865.080883 | 137.155356 |
| 1 | 2 | 2010-02-05 | 2012-01-27 | 2012-02-03 | 2012-04-27 | 1965.952051 | 1969.586155 | 4035.608919 | 839.189260 |
| 2 | 3 | 2010-02-05 | 2012-04-27 | 2012-05-04 | 2012-07-27 | 1585.766265 | 1585.766265 | 3052.148316 | 952.329147 |
| 3 | 4 | 2010-02-05 | 2012-07-27 | 2012-08-03 | 2012-10-26 | 1436.435312 | 1435.277361 | 2961.666930 | 974.137173 |
wmae 1834.753547
mae 1796.0386
rmse 3728.626262
runtime_sec 725.702734
Name: 4, dtype: object
9 Model 6: AdaBoost Anomaly Baseline (Phase 7)
AdaBoost builds an additive ensemble of shallow trees, where each stage focuses more on difficult residual patterns from previous stages.
9.1 Mathematical Formulation
With base learners (h_m):
\[ F_M(x)=\sum_{m=1}^{M}\nu_m h_m(x) \]
where stage weights depend on chosen boosting loss and learning rate. In practice here, (h_m) are depth-limited regression trees.
9.2 Plain-Language Meaning of Terms
h_m(x): base learner at stagem(small regression tree).nu_m: effective contribution of stagemto the final prediction.M: number of boosting stages.learning_rate: shrinkage factor that controls how aggressively each stage updates the model.- Boosting logic: each new tree focuses more on errors made by previous trees.
9.3 Workflow
flowchart LR A[Read train/test parquet] --> B[Compute climatology and anomaly target] B --> C[Create lag1/lag2 + calendar + exogenous features] C --> D[Median imputation] D --> E[Train AdaBoost regressor on fold train] E --> F[Recursive validation rollout] F --> G[OOF metrics and predictions] G --> H[Refit on full train and produce test forecasts]
9.4 Tuned Parameters and Effect
| Parameter | Value | How it works | |
|---|---|---|---|
| 0 | n_estimators | 300 | Number of boosting stages. |
| 1 | learning_rate | 0.03 | Shrinkage per stage; smaller needs more estima... |
| 2 | max_depth | 3 | Depth of each base regression tree. |
| 3 | loss | linear | Boosting loss that controls how hard examples ... |
| 4 | lags | [1, 2] | Autoregressive lag inputs for recursive foreca... |
{'model': 'adaboost',
'target': 'sales_anom',
'features': ['lag1',
'lag2',
'week_of_year',
'month',
'is_holiday_int',
'temp_anom',
'fuel_anom',
'MarkDown1',
'MarkDown2',
'MarkDown3',
'MarkDown4',
'MarkDown5',
'CPI',
'Unemployment'],
'lags': [1, 2],
'n_estimators': 300,
'learning_rate': 0.03,
'max_depth': 3,
'loss': 'linear',
'n_folds': 4,
'val_weeks': 13,
'max_series': None,
'train_path': 'train_feat.parquet',
'test_path': 'test_feat.parquet'}
| fold | train_start | train_end | val_start | val_end | wmae | mae | rmse | runtime_sec | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2010-02-05 | 2011-10-28 | 2011-11-04 | 2012-01-27 | 2479.961992 | 2278.508843 | 4905.691398 | 144.954249 |
| 1 | 2 | 2010-02-05 | 2012-01-27 | 2012-02-03 | 2012-04-27 | 2197.511715 | 2189.320166 | 4409.023648 | 189.685843 |
| 2 | 3 | 2010-02-05 | 2012-04-27 | 2012-05-04 | 2012-07-27 | 2014.048705 | 2014.048705 | 3866.751795 | 214.710872 |
| 3 | 4 | 2010-02-05 | 2012-07-27 | 2012-08-03 | 2012-10-26 | 1865.985156 | 1843.294438 | 3739.207824 | 239.991155 |
wmae 2139.376892
mae 2081.293038
rmse 4230.168666
runtime_sec 197.33553
Name: 4, dtype: object
10 Final Comparative
10.1 Full-Data Comparison (Phases 1, 2, 3, 4, 5, 7 + Phase 1-lite Diagnostic)
| Model | Mean WMAE | Mean MAE | Mean RMSE | |
|---|---|---|---|---|
| 0 | ETS (Phase 5) | 1834.753547 | 1796.038600 | 3728.626262 |
| 1 | Random Forest (Phase 4) | 2057.750915 | 2006.732036 | 4130.493432 |
| 2 | Elastic Net (Phase 3) | 2132.272878 | 2082.729444 | 4195.010969 |
| 3 | AdaBoost (Phase 7) | 2139.376892 | 2081.293038 | 4230.168666 |
| 4 | Local Linear Lite (Phase 1-lite) | 2161.186467 | 2104.994105 | 4336.037305 |
| 5 | Structural AR (Phase 2) | 2187.483231 | 2146.618420 | 4189.028433 |
| 6 | Local Linear Anomaly (Phase 1) | 10075.781360 | 10206.203892 | 18921.817643 |
11 Conclusion
- Full-data comparison is now apples-to-apples for all six models.
- All tabular baselines share the same anomaly feature interface with
lag1andlag2. - The ranking table above is the reference for selecting the best model in this run.